There is a need for up-to-date assessments and maps of soil properties and land health at scales relevant for decision-making and management, including for properties that are dynamic and hence change in response to management. Also, there is a need for approaches to soil mapping that capture the ever increasing effects that humans are having on the environment in general and specifically on soil properties worldwide. In this paper, we develop models for digital soil mapping based on remote sensing data from the Moderate Resolution Imaging Spectroradiometer (MODIS) platform for Africa. The article presents maps of soil organic carbon (SOC), pH, sand and sum of exchangeable bases, as well as prevalence of root-depth restrictions in the upper 50 cm of the soil profile. Prediction models were developed based on spatially balanced field survey data, representing all major climate zones on the continent. The prediction models for soil property mapping performed well, with overall RMSEP values of 10.6, 0.34, 9.1, and 6.5 for SOC, pH, sand, and sum of bases, respectively. The accuracy of the prediction model for root-depth restrictions was 77%, with an AUC of 0.85 and Cohen's kappa value of 0.52 when averaged across predictions run on independent test data. The methods and maps developed can provide much improved identification of soil and land health constraints, and spatial targeting of land management interventions at various scales, informing both policy and practice.
DOI:
https://doi.org/10.1016/j.geoderma.2015.06.023Altmetric score:
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Publication year
2016
Authors
Vågen, T-G.; Winowiecki, L.A.; Tondoh, J.E.; Desta, L.T.; Gumbricht, T.
Language
English
Keywords
soil, landscape, soil properties, mapping, land degradation, remote sensing, soil organic carbon
